Datasets:

TTS-AGI
/

moss-emolia-elise-hq-captioned

pretty_name: MOSS · Emolia + Elise + Inline-Bursts (HQ, captioned)
license: other
language:
  - de
  - en
  - multilingual
task_categories:
  - text-to-speech
  - audio-classification
  - automatic-speech-recognition
size_categories:
  - 1K<n<10K
tags:
  - audio
  - speech
  - voice-acting
  - expressive-tts
  - emotion
  - vocal-bursts
  - moss-tts
  - voicenet
  - emonet
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.tar
  - config_name: metadata
    data_files:
      - split: train
        path: metadata.parquet
  - config_name: tokenized
    data_files:
      - split: train
        path: tokenized_audio.parquet

MOSS · Emolia + Elise + Inline-Bursts — HQ, captioned

A high-quality, richly captioned slice of the MOSS-local voice-acting corpus: expressive speech clips scored by a panel of acoustic detectors, filtered to the top by a composite reward, and captioned in the voice-acting format (a "how the voice sounds / how to perform it" description plus the script with inline vocal-burst tags). Audio is shipped both as flac (WebDataset tars) and as pre-computed MOSS-Audio-Tokenizer codes for direct TTS training.

6,263 clips drawn from three sources:

`source_dataset`	clips	what it is
`emolia`	3,768	emotion-bucketed expressive speech (Emolia)
`mossinline`	1,800	speech with inline vocal bursts (laughs, sighs, gasps…)
`elise`	695	Elise / DramaBox-style dramatic delivery

Every clip was scored on VoiceNet (57 perceptual voice dimensions), Empathic-Insight-Voice-Plus / EmoNet (40 emotions + Arousal / Valence / Authenticity), a genuineness score (felt vs. performed), a vocal-burst blend / naturalness score, and WER against the reference text; the composite score (range 0.3–16.0, mean ≈ 10.4) selects the high-quality tail.

Files

data/data-000{0..3}.tar — WebDataset shards. Each member pair is <sample_key>.flac (audio) + <sample_key>.json (per-clip metadata). ~2,000 clips per shard.
metadata.parquet — one row per clip with the full annotation set (scores + captions). Join to the audio via sample_key.
tokenized_audio.parquet — the same clips as MOSS-Audio-Tokenizer codes, ready for MOSS-TTS training (no raw audio needed). Join via key (== sample_key).

`metadata.parquet` columns

column	meaning
`sample_key` / `id`	unique id, `"<source>__<local-id>"` (e.g. `emolia__…`); matches the tar member and the tokenized `key`
`source_dataset`	`emolia` / `mossinline` / `elise`
`text`	reference transcript
`ext`	audio extension (`flac`)
`vn_*` (57)	VoiceNet perceptual dimensions (warmth, roughness, tempo, register, resonance, …), ~0–6 scale
`ei_*` (43)	Empathic-Insight-Voice-Plus: 40 EmoNet emotions + `ei_Arousal`, `ei_Valence`, `ei_Authenticity`
`genu`	genuineness (felt vs. performed)
`blend`	vocal-burst blend / naturalness (0–10)
`bude_caption`	free-text BUD-E-Whisper caption
`inline_burst`	transcript with inline vocal-burst tags
`procedural_caption`	rule-based voice-acting caption: `GENERAL:` (how the voice sounds) + `SCRIPT:` (delivery cues + text)
`voice_acting_caption`	LLM-naturalised rewrite of the procedural caption (same structure, fluent wording)
`score`	composite selection reward (higher = higher quality)

`tokenized_audio.parquet` columns

column	meaning
`key`	id (matches `sample_key`)
`target_codes`	MOSS-Audio-Tokenizer codes for the target clip, `int16` bytes, shape `[target_frames, n_codebooks]`
`target_frames`	number of code frames
`ref_codes` / `ref_frames`	optional reference-voice codes (often empty)
`text`	reference transcript
`procedural_caption`, `voice_acting_caption`	as above
`source_dataset`	`emolia` / `mossinline` / `elise`

Usage

Stream the audio + captions (WebDataset):

from datasets import load_dataset
ds = load_dataset("TTS-AGI/moss-emolia-elise-hq-captioned", split="train", streaming=True)
ex = next(iter(ds))
print(ex["json"]["voice_acting_caption"])
ex["flac"]["array"], ex["flac"]["sampling_rate"]

Load just the scores + captions:

ds = load_dataset("TTS-AGI/moss-emolia-elise-hq-captioned", "metadata", split="train")

Decode the MOSS tokens for training:

import numpy as np, pandas as pd
df = pd.read_parquet("tokenized_audio.parquet")
row = df.iloc[0]
codes = np.frombuffer(row["target_codes"], np.int16).reshape(row["target_frames"], -1)

Notes

Captions and scores are model-generated (VoiceNet / EmoNet / genuineness / blend detectors + procedural templating + LLM rewrite) and are not manually verified.
Part of the MOSS-local voice-acting data-generation effort. license: other — see the source datasets for provenance and terms.